A Self Updating Clustering Algorithm -SUP Based on -Divergence with Application to Cryo-EM Images


I-Ping Tu

16:20:00 - 17:10:00

308 , Mathematics Research Center Building (ori. New Math. Bldg.)

In the past decades, cryo-electron microscopy (cryo-EM) has become a powerful tool for obtaining high resolution three- dimension (3-D) structures of biological macro-molecules. A cryo-EM data set usually contains at least thousands of 2-D projection images of free oriented particles. The characteristics of these projections include having low signal-to-noise ratio, containing many misaligned images as outliers, and consisting of a large number of clusters due to free orientations. Clustering analysis is a necessary step to group the similar orientation images for noise deduction. In this article, we propose a clustering algorithm, gamma-SUP, by implementing a minimum gamma- divergence on a mixture of q-Gaussian family through a self- updating process. gamma-SUP copes well with the cryo-EM images by its advantages as follows. (a) It resolves the sensitivity issue of choosing the number of clusters and cluster initials. (b) It sets a hard influence range for each component in the mixture model and hence leads to a robust procedure for learning each of the local clusters. (c) It performs a soft rejection by down weighting deviant points from cluster centers and further enhances the robustness. (d) At each iteration, it shrinks the mixture model parameter estimates toward cluster centers, and improves the efficiency of mixture estimation.